… Thus, we review the ARIMA, LSTM, CNN, Hybrid LSTM and Hybrid CNN models and … as
another deep learning model in terms of predicting accuracy. The LSTM, CNN, and their hybrid …

… hybrid deep learning models to provide the generic activity recognition framework and tune
the performance. The following combination of the deep learning … , the ensemble learning is …

… In this paper, we presented a hybrid deep learning technique that captures the spatial-spectral
features of images for the classification of distraction postures. Our architecture …

… We develop a novel hybrid deep learning method to improve forecasts of … , a hybrid volatility
prediction model is developed in this study by synthesizing the state-of-art deep learning …

… propose a hybrid deep learning approach based … approach is extensively evaluated on real
PV data in Limberg, Belgium, and numerical results demonstrate that the proposed approach …

… (2) Method: This paper proposes a hybrid deep learning-based approach to automate the
detection and classification process. This paper makes two-fold contributions. First, 1D ECG …

… This proposed hybrid method is utilized to extract scene … double feature extraction hybrid
deep learning approach to classify … This research work has developed a novel hybrid framework …

… In this study, we propose a hybrid deep learning technique that utilizes two deep neural …
model is summarized as follows: • We propose a hybrid deep learning model named ‘‘AE-MLP’’ …

… Accordingly, various artificial intelligence techniques and deep learning have been … In
this paper, we propose a hybrid deep learning approach using deep convolutional neural …

… , and they are continually evolving for the learning approach. We can generate binary images
… in the area of complex wavelets for image approximations with a deep learning approach. …